What is required for a dissertation data analysis chapter

The data analysis is a very important part of your dissertation because this is where you present and analyze your findings. Here is a dissertation chapter outline for your dissertation chapter on data analysis.

  • Relevance: Don’t follow your data blindly; make sure that your research objectives are used as a guide to determine what you include in your final analysis. All of the data that you present should be appropriate and relevant to your main aim. Data that is not relevant will inform the review board that you are incoherent and incapable of focusing.
  • Analysis: Make sure that you use methods that are not only appropriate to the aims of your research but also to the type of data collected. You should justify and explain these methods the same way you justified your collection methods. The main aim here is to show that you didn’t just choose your method randomly, but that you chose them as a result of critical reasoning and research into the best methods.
  • Quantitative work: Technical and scientific research typically use quantitative data, this method requires in-depth statistical analysis. Through the collection and analysis of quantitative data, you can effectively draw conclusions that also work outside of the sample. The social sciences refer to this approach as the “scientific method.”
  • Qualitative work: Qualitative data is typically non numerical and is also referred to as “soft data.” This does not mean that it does not require the same analytical standards as quantitative data. The aim of this type of data is to discover a deeper meaning behind the data.
  • Detail: Information never speaks for itself and this is one of the common mistakes when made analyzing qualitative studies. Students are known for presenting a several quotes and assuming that this is sufficient. All of the data presented should be analyzed in-depth if this is what you intend on using to refute or support an academic position. You should be capable of demonstrating a critical perspective and that not only do you acknowledge the strengths but that you also acknowledge the weaknesses of your data. All of which is a demonstration of your academic credibility.
  • Presentational devices: Large amounts of information can be difficult to present in an intelligible way. It is important that this problem is addressed by considering all of the possible means in which data can be presented. This includes diagrams, charts, graphs, formulae and quotes, all of which have their own advantages. It is important that you always keep your reader in mind when you are presenting your data because they are the ones who need to understand it.
#